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Conference Paper: One-pass wavelet synopses for maximum-error metrics
Title | One-pass wavelet synopses for maximum-error metrics |
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Authors | |
Issue Date | 2005 |
Citation | Vldb 2005 - Proceedings Of 31St International Conference On Very Large Data Bases, 2005, v. 1, p. 421-432 How to Cite? |
Abstract | We study the problem of computing wavelet-based synopses for massive data sets in static and streaming environments. A compact representation of a data set is obtained after a thresholding process is applied on the coefficients of its wavelet decomposition. Existing polynomial-time thresholding schemes that minimize maximum error metrics are disadvantaged by impracticable time and space complexities and are not applicable in a data stream context. This is a cardinal issue, as the problem at hand in its most practically interesting form involves the time-efficient approximation of huge amounts of data, potentially in a streaming environment. In this paper we fill this gap by developing efficient and practicable wavelet thresholding algorithms for maximum-error metrics, for both a static and a streaming case. Our algorithms achieve near-optimal accuracy and superior runtime performance, as our experiments show, under frugal space requirements in both contexts. |
Persistent Identifier | http://hdl.handle.net/10722/93155 |
References |
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Karras, P | en_HK |
dc.contributor.author | Mamoulis, N | en_HK |
dc.date.accessioned | 2010-09-25T14:52:33Z | - |
dc.date.available | 2010-09-25T14:52:33Z | - |
dc.date.issued | 2005 | en_HK |
dc.identifier.citation | Vldb 2005 - Proceedings Of 31St International Conference On Very Large Data Bases, 2005, v. 1, p. 421-432 | en_HK |
dc.identifier.uri | http://hdl.handle.net/10722/93155 | - |
dc.description.abstract | We study the problem of computing wavelet-based synopses for massive data sets in static and streaming environments. A compact representation of a data set is obtained after a thresholding process is applied on the coefficients of its wavelet decomposition. Existing polynomial-time thresholding schemes that minimize maximum error metrics are disadvantaged by impracticable time and space complexities and are not applicable in a data stream context. This is a cardinal issue, as the problem at hand in its most practically interesting form involves the time-efficient approximation of huge amounts of data, potentially in a streaming environment. In this paper we fill this gap by developing efficient and practicable wavelet thresholding algorithms for maximum-error metrics, for both a static and a streaming case. Our algorithms achieve near-optimal accuracy and superior runtime performance, as our experiments show, under frugal space requirements in both contexts. | en_HK |
dc.language | eng | en_HK |
dc.relation.ispartof | VLDB 2005 - Proceedings of 31st International Conference on Very Large Data Bases | en_HK |
dc.title | One-pass wavelet synopses for maximum-error metrics | en_HK |
dc.type | Conference_Paper | en_HK |
dc.identifier.email | Mamoulis, N:nikos@cs.hku.hk | en_HK |
dc.identifier.authority | Mamoulis, N=rp00155 | en_HK |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.scopus | eid_2-s2.0-33745616563 | en_HK |
dc.identifier.hkuros | 103331 | en_HK |
dc.relation.references | http://www.scopus.com/mlt/select.url?eid=2-s2.0-33745616563&selection=ref&src=s&origin=recordpage | en_HK |
dc.identifier.volume | 1 | en_HK |
dc.identifier.spage | 421 | en_HK |
dc.identifier.epage | 432 | en_HK |
dc.identifier.scopusauthorid | Karras, P=14028488200 | en_HK |
dc.identifier.scopusauthorid | Mamoulis, N=6701782749 | en_HK |